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With the increasing amount of information, document clustering is applied to the information for easy recognition of the relevance of content. In this paper, the authors present a clustering algorithm that uses semantic similarity measure. Thus, a methodology to interpret and cluster knowledge documents using ontology is presented. The Particle Swarm Optimization (PSO) clustering algorithm can be applied to the annotated documents. They further evaluate the performance of using ontology with the classical K-means and Particle Swarm Optimization clustering algorithm. Their results show that using ontology with the particle swarm optimization performs better than K-means algorithm. The accuracy of clustering has been computed before and after using ontology. The arrived results were significant and promising.
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